The Experts below are selected from a list of 46023 Experts worldwide ranked by ideXlab platform
Mark Johnston - One of the best experts on this subject based on the ideXlab platform.
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keypoints detection and feature extraction a dynamic genetic programming approach for evolving rotation invariant texture image descriptors
IEEE Transactions on Evolutionary Computation, 2017Co-Authors: Harith Alsahaf, Ausama Alsahaf, Mengjie Zhang, Mark JohnstonAbstract:The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical Domain. Designing a good feature set, on the other hand, is very hard and often requires Domain Expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, Domain-Expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilize genetic programming to automatically construct a rotation-invariant image descriptor by synthesizing a set of formulas using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight Domain-Expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.
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automatically evolving rotation invariant texture image descriptors by genetic programming
IEEE Transactions on Evolutionary Computation, 2017Co-Authors: Harith Alsahaf, Ausama Alsahaf, Mark Johnston, Mengjie ZhangAbstract:In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a Domain Expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires Domain-Expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labeled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel genetic programming-based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation and is compared with seven Domain-Expert designed descriptors. The results show that the proposed method is robust to rotation and has significantly outperformed, or achieved a comparable performance to, the baseline methods.
Mengjie Zhang - One of the best experts on this subject based on the ideXlab platform.
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keypoints detection and feature extraction a dynamic genetic programming approach for evolving rotation invariant texture image descriptors
IEEE Transactions on Evolutionary Computation, 2017Co-Authors: Harith Alsahaf, Ausama Alsahaf, Mengjie Zhang, Mark JohnstonAbstract:The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical Domain. Designing a good feature set, on the other hand, is very hard and often requires Domain Expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, Domain-Expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilize genetic programming to automatically construct a rotation-invariant image descriptor by synthesizing a set of formulas using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight Domain-Expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.
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automatically evolving rotation invariant texture image descriptors by genetic programming
IEEE Transactions on Evolutionary Computation, 2017Co-Authors: Harith Alsahaf, Ausama Alsahaf, Mark Johnston, Mengjie ZhangAbstract:In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a Domain Expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires Domain-Expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labeled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel genetic programming-based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation and is compared with seven Domain-Expert designed descriptors. The results show that the proposed method is robust to rotation and has significantly outperformed, or achieved a comparable performance to, the baseline methods.
Harith Alsahaf - One of the best experts on this subject based on the ideXlab platform.
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keypoints detection and feature extraction a dynamic genetic programming approach for evolving rotation invariant texture image descriptors
IEEE Transactions on Evolutionary Computation, 2017Co-Authors: Harith Alsahaf, Ausama Alsahaf, Mengjie Zhang, Mark JohnstonAbstract:The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical Domain. Designing a good feature set, on the other hand, is very hard and often requires Domain Expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, Domain-Expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilize genetic programming to automatically construct a rotation-invariant image descriptor by synthesizing a set of formulas using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight Domain-Expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.
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automatically evolving rotation invariant texture image descriptors by genetic programming
IEEE Transactions on Evolutionary Computation, 2017Co-Authors: Harith Alsahaf, Ausama Alsahaf, Mark Johnston, Mengjie ZhangAbstract:In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a Domain Expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires Domain-Expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labeled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel genetic programming-based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation and is compared with seven Domain-Expert designed descriptors. The results show that the proposed method is robust to rotation and has significantly outperformed, or achieved a comparable performance to, the baseline methods.
Ausama Alsahaf - One of the best experts on this subject based on the ideXlab platform.
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keypoints detection and feature extraction a dynamic genetic programming approach for evolving rotation invariant texture image descriptors
IEEE Transactions on Evolutionary Computation, 2017Co-Authors: Harith Alsahaf, Ausama Alsahaf, Mengjie Zhang, Mark JohnstonAbstract:The goodness of the features extracted from the instances and the number of training instances are two key components in machine learning, and building an effective model is largely affected by these two factors. Acquiring a large number of training instances is very expensive in some situations such as in the medical Domain. Designing a good feature set, on the other hand, is very hard and often requires Domain Expertise. In computer vision, image descriptors have emerged to automate feature detection and extraction; however, Domain-Expert intervention is typically needed to develop these descriptors. The aim of this paper is to utilize genetic programming to automatically construct a rotation-invariant image descriptor by synthesizing a set of formulas using simple arithmetic operators and first-order statistics, and determining the length of the feature vector simultaneously using only two instances per class. Using seven texture classification image datasets, the performance of the proposed method is evaluated and compared against eight Domain-Expert hand-crafted image descriptors. Quantitatively, the proposed method has significantly outperformed, or achieved comparable performance to, the competitor methods. Qualitatively, the analysis shows that the descriptors evolved by the proposed method can be interpreted.
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automatically evolving rotation invariant texture image descriptors by genetic programming
IEEE Transactions on Evolutionary Computation, 2017Co-Authors: Harith Alsahaf, Ausama Alsahaf, Mark Johnston, Mengjie ZhangAbstract:In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a Domain Expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires Domain-Expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labeled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel genetic programming-based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation and is compared with seven Domain-Expert designed descriptors. The results show that the proposed method is robust to rotation and has significantly outperformed, or achieved a comparable performance to, the baseline methods.
Claus Skaanning - One of the best experts on this subject based on the ideXlab platform.
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A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters
arXiv: Artificial Intelligence, 2013Co-Authors: Claus SkaanningAbstract:This paper describes a Domain-specific knowledge acquisition tool for intelligent automated troubleshooters based on Bayesian networks. No Bayesian network knowledge is required to use the tool, and troubleshooting information can be specified as natural and intuitive as possible. Probabilities can be specified in the direction that is most natural to the Domain Expert. Thus, the knowledge acquisition efficiently removes the traditional knowledge acquisition bottleneck of Bayesian networks.